}

  open fun logCloseableLeak(
    message: String,
    stackTrace: Any?,
  ) {
    var logMessage = message
    if (stackTrace == null) {
      logMessage += " To see where this was allocated, set the OkHttpClient logger level to " +
        "FINE: Logger.getLogger(OkHttpClient.class.getName()).setLevel(Level.FINE);"
    }
    log(logMessage, WARN, stackTrace as Throwable?)
  }

        }
    }


    /**
     * @param stackTrace
     * @return
     */
    private static StackTraceElement[] truncateTrace ( StackTraceElement[] stackTrace ) {

        int s = 2;
        int e = stackTrace.length;

        for ( int i = s; i < e; i++ ) {
            StackTraceElement se = stackTrace[ i ];

  virtual absl::Status OutputLength(const char* output_name, int* length) = 0;

  // Set stack trace to be used for potential async error reporting.
  virtual void SetStackTrace(ManagedStackTrace stack_trace) = 0;

  virtual const tensorflow::AbstractOpAttrs* GetOpAttrs() const = 0;
  virtual void AddAttrs(const AbstractOpAttrs* op_attrs) = 0;

  virtual void SetCancellationManager(

  - type: textarea
    id: actual-behavior
    attributes:
      label: "What did you see happen?"
      description: Command invocations and their associated output, functions with their arguments and return results, full stacktraces for panics (upload a file if it is very long), etc. Prefer copying text output over using screenshots.
    validations:
      required: true

  - type: textarea
    id: expected-behavior
    attributes:

 *    thread's [uncaught exception handler][Thread.UncaughtExceptionHandler]. By default this
 *    crashes the application on Android and prints a stacktrace on the JVM. (Crash reporting
 *    libraries may customize this behavior.)
 *
 * A good way to signal a failure is with a synthetic HTTP response:
 *
 * ```kotlin
 *   @Throws(IOException::class)

#### Blocking APIs

Blocking APIs are convenient because you get top-to-bottom procedural code without indirection. Network calls work like regular method calls: ask for data and it is returned. If the request fails, you get a stacktrace right where the call was made.

Blocking APIs may be inefficient because you hold a thread idle while waiting on the network. Threads are expensive because they have both a memory overhead and a context-switching overhead.

        def projectDir = 'C:\\some\\agent\\workspace'

        expect:

NOTE: Due to the project size, the very first import can take a while and IntelliJ might become unresponsive for several seconds during this period.

          distribution: 'zulu'
          java-version: 17

      - name: Setup Gradle
        uses: gradle/actions/setup-gradle@v4

      - name: Upload Artifacts
        run: ./gradlew clean publish --stacktrace
        env:
          ORG_GRADLE_PROJECT_mavenCentralUsername: ${{ secrets.SONATYPE_NEXUS_USERNAME }}
          ORG_GRADLE_PROJECT_mavenCentralPassword: ${{ secrets.SONATYPE_NEXUS_PASSWORD }}

        R invokerRequest = context.invokerRequest;
        Logger logger = context.logger;
        if (invokerRequest.options().showErrors().orElse(false)) {
            logger.info("Error stacktraces are turned on.");
        }
        if (context.invokerRequest.options().verbose().orElse(false)) {
            logger.debug("Message scheme: " + (MessageUtils.isColorEnabled() ? "color" : "plain"));